Pushing Distributed Vibration Analysis to the Edge with a Low-Resolution Companding Autoencoder: Industrial IoT for PHM

Abstract

The Industrial Internet-of-Things (IIoT) has disrupted the
way of collecting physical data for predictive maintenance
purposes. At present, networks of intelligent wireless sensors
are pervasive, finding success in many environments and
industries, including the railways. However, when it comes
to data-intensive applications like vibration monitoring that
require the delivery of large amounts of records, the limitations
of these devices arise. The shortfalls are mainly driven
by the low-bandwidth transmission capacity of their radio interfaces,
and the low-power features of their battery-operated
(and/or energy-harvested) electronics. In sight of these limited
resources, this article explores a vibration data compression
strategy for diagnosis purposes. To maximise the amount
of transferred information with the least amount of bytes this
method works in three stages: first, it extracts the most useful
features for vibration-based analytics. Then, it compresses
the raw signal waveform using an Autoencoder neural network
with an undercomplete representation, assessing its optimum
regularisation approach: the denoising, sparse, and
contractive configurations. Finally, it reduces the resolution
of the compressed data by quantising all the resulting real values
into single-byte unsigned integers. The proposed strategy
is evaluated with a dataset of railway axle bearings with different
levels of degradation. The results of the analysis prove
that with compression rates up to 10 the vibration signals are
practically unaffected by this procedure, allowing for many
diagnosis goals like anomaly detection, fault location, and
severity appraisal. This approach yields a wide range of business
opportunities for on-board predictive maintenance with
IIoT technology.